US 12,231,616 B2
Sparse and/or dense depth estimation from stereoscopic imaging
Kangkang Wang, San Jose, CA (US); Alexander Ngai, Irvine, CA (US); and Zachary Beaver, San Francisco, CA (US)
Assigned to Deere & Company, Moline, IL (US)
Filed by Deere & Company, Moline, IL (US)
Filed on Feb. 8, 2022, as Appl. No. 17/667,287.
Application 17/667,287 is a continuation in part of application No. 17/513,498, filed on Oct. 28, 2021, granted, now 11,995,859.
Prior Publication US 2023/0133026 A1, May 4, 2023
Int. Cl. H04N 13/363 (2018.01); G06N 20/00 (2019.01); G06V 10/74 (2022.01); G06V 10/82 (2022.01); H04N 13/178 (2018.01); H04N 13/183 (2018.01); H04N 13/275 (2018.01); H04N 13/00 (2018.01)
CPC H04N 13/363 (2018.05) [G06N 20/00 (2019.01); G06V 10/761 (2022.01); G06V 10/82 (2022.01); H04N 13/178 (2018.05); H04N 13/183 (2018.05); H04N 13/275 (2018.05); H04N 2013/0081 (2013.01)] 18 Claims
OG exemplary drawing
 
13. A system for generating a pair of synthetic stereoscopic training images, the system comprising one or more processors and memory storing instructions that, in response to execution by the one or more processors, cause the one or more processors to:
select one or more characteristics for generation of one or more images of three-dimensional synthetic plants based on an environmental consideration of an agricultural area, the one or more characteristics to correspond to data to generate a plant model;
generate the one or more images of the three-dimensional synthetic plants in a three-dimensional space based on the one or more characteristics, wherein the one or more three-dimensional synthetic plants include homogenous and densely distributed synthetic plant parts;
project the one or more images of the three-dimensional synthetic plants onto a two-dimensional plane from a first perspective in the three-dimensional space to form a first synthetic stereoscopic image of the pair;
project the one or more images of the three-dimensional synthetic plants onto another two-dimensional plane from a second perspective in the three-dimensional space to form a second synthetic stereoscopic image of the pair;
annotate pixels of the first and second synthetic stereoscopic images that represent same individual synthetic plant parts to create a mapping between depictions of the same individual synthetic plant parts across the first and second synthetic stereoscopic images; and
train a feature matching machine learning model based on the mapping.